Plasma miRNA profiling in patients with hepetocellular carcinoma and liver cirrhosis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE294055 | GEO | 2025/06/12
REPOSITORIES: GEO
Similar Datasets
Project description:Exosomal microRNAs have recently been studied as potential diagnostic marker for various malignancies, including hepatocellular carcinoma (HCC). The aim of this study was to investigate serum exosomal microRNA profiles as HCC diagnostic marker. Transmission electron microscopy and western blot were used to identify serum exosomes. Deep sequencing was performed to screen differentially expressed microRNAs between HCC (n=5) and liver cirrhosis (LC, n=5) group. Three upregulated and two downregulated microRNAs were selected for qPCR analysis. The levels of selected microRNAs were normalized to Caenorhabditis elegans miR-39 microRNA mimics. Serum exosomal level of miR-122, miR-148a, and miR-1246 were further analyzed and significantly higher in HCC than LC and normal control (NC) group (P<0.001), but not different from chronic hepatitis group(p>0.05). The receiver operating characteristic curve was used to evaluate diagnostic perfromance of candidate microRNAs. Area under the curve (AUC) of miR-148a was 0.891 [95 % confidence interval (CI), 0.809-0.947] in discriminating HCC from LC, remarkably higher than alpha fetoprotein (AFP) (AUC: 0.712, 95 % CI: 0.607-0.803). Binary logistic regression was adpoted to establish the diagnostic model for discriminating HCC from LC. And the combination of miR-122, miR-148a and AFP increased the AUC to 0.931 (95% CI, 0.857-0.973), which can also be applied for distinguishing early HCC from LC. miR-122 was the best for differentiating HCC from NC (AUC: 0.990, 95% CI, 0.945-1.000). These data suggests that serum exosomal microRNAs signature or their combination with traditional biomarker may be used as a suitable peripheral screening tool for HCC.
2018-10-01 | GSE104251 | GEO
Project description:Hepatocellular carcinoma (HCC) is one of the leading cause of cancer-related mortality worldwide. Liver cirrhosis is well-established risk factor of HCC development, although prevention of HCC in cirrhosis patients is challenging. By utilizing bioinformatic compound screening based on a clinical HCC-risk-predicitve gene signature, we identified captopril as a potential HCC chemoprevention agent. We tested the drug in rats with diethylnitrosamine (DEN)-induced cirrhosis and HCC, and confirmed its HCC-preventive effect.
2022-08-02 | GSE157919 | GEO
Project description:A quantitative label-free proteome analysis was performed using plasma samples from 22 hepatitis-C virus (HCV)-induced liver cirrhosis patients, 16 HCV-positive hepatocellular carcinoma patients with underlying cirrhosis and 18 healthy controls. Plasma microparticles (PMPS) were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS. A quantitative label-free proteome analysis was performed using plasma samples from 22 hepatitis-C virus (HCV)-induced liver cirrhosis patients, 16 HCV-positive hepatocellular carcinoma patients with underlying cirrhosis and 18 healthy controls. Plasma microparticles (PMPS) were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS.
2017-06-21 | PXD005777 | Pride
Project description:Plasma miRNA profiling in patients with hepetocellular carcinoma and liver cirrhosis
| PRJNA1247971 | ENA
Project description:A quantitative label-free proteome analysis was performed using plasma samples from 22 hepatitis-C virus (HCV)-induced liver cirrhosis patients, 16 HCV-positive hepatocellular carcinoma patients with underlying cirrhosis and 18 healthy controls. Plasma microparticles (PMPS) were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS. A quantitative label-free proteome analysis was performed using plasma samples from 22 hepatitis-C virus (HCV)-induced liver cirrhosis patients, 16 HCV-positive hepatocellular carcinoma patients with underlying cirrhosis and 18 healthy controls. Plasma microparticles (PMPS) were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS.
2020-03-12 | MSV000085094 | MassIVE
Project description:Biomarker studies for early stage or preclinical hepatocellular carcinomas (HCC) are hindered by the difficulties of obtaining samples from asymptomatic individuals. We established animal models using irradiated mice to investigate circulating miRNA as non-invasive markers for detection of early stage HCC. We hypothesized that certain miRNAs that play pivotal roles in molecular pathways are conserved across species and identification of these miRNAs will facilitate studying human markers in mice. To test the hypothesis, we performed weighted gene co-expression analysis by integrating circulating miRNA and tumor gene expression profiles from individual mice and discovered hub miRNAs in highly correlated expression modules. We validated the hub miRNAs using F2 hybrid mice derived from radiogenic HCC susceptible and resistant founders and identified 38 circulating miRNA markers associated with radiation-induced HCC. Through literacy search, we selected 10 human HCC-associated circulating miRNAs that had been validated in multiple independent patient cohorts. Nine of the 10 human markers overlapped with the mouse hub miRNAs, indicating the feasibility of using mouse model to study human circulating HCC markers. Using serially collected plasma samples from irradiated mice, we studied the kinetics of circulating miRNAs. We found that the mouse plasma levels of 4 human circulating markers, miR-122-5p, miR-100-5p, miR-34a-5p and miR-365-3p increased linearly as the time approaching towards HCC detection, indicating the correlations of the 4 miRNAs with oncogenic progression. Estimation of change points in the kinetics of the 4 circulating miRNAs suggested the changes started months before HCC detection, ranging from 17.5 to 6.8 months. Our data demonstrated that the 4 circulating miRNAs were sensitive biomarkers potentially valuable for the screening of early stage HCC.
2022-07-06 | GSE196193 | GEO
Project description:In cancer management, early and accurate diagnosis of hepatocellular carcinoma (HCC) is important for enhancing survival rate of patients. Currently, serum alpha-fetoprotein (AFP) is the only one biomarker for detection of HCC. However, serum AFP is not satisfactory for diagnosis of HCC due to its low accuracy (about 60-70%). In this study, we collected 109 serum samples (discovery set) from healthy control (HC) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC) and HCC, and analyzed them with custom lncRNA microarray. Profiling analysis shows 181 differentially expressed lncRNAs between HCs and patients with CHB, LC and HCC. Then a 48-lncRNA diagnostic signature was identified with 100% predictive accuracy for all subjects in the discovery set. This diagnostic signature was verified with a cross-validation analysis in the discovery set. To further corroborate the signature, we gathered another 66 serum samples (validation set) and also analyzed them with microarray. The result indicates that the same signature has similar diagnostic accuracy for HC (100%), CHB (73%), LC (88%) and HCC (95%), implying a reproducible diagnostic biomarker for HCC. Receiver operating characteristic (ROC) analysis exhibits that this signature has significantly higher diagnostic accuracy for HCC and non-cancerous subjects (area under curve [AUC]: 0.994) than AFP (AUC: 0.773) in the discovery set and this was also verified in the validation set (0.964 vs 0.792). More importantly, the signature detected small HCC (<3cm) with 100% (13/13) accuracy while AFP with only 61.5% (8/13). Altogether, this study demonstrates that the serum 48-lncRNA signature is not only a powerful and sensitive biomarker for diagnosis of HCC but also a potential biomarker for LC. ***************************************************************** Submitter declares these data are subject to patent number ZL 2016 1 0397094. *****************************************************************
2022-10-15 | GSE78160 | GEO
Project description:<p><strong>BACKGROUND & AIMS: </strong>The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities.</p><p><strong>METHODS: </strong>We developed a multicenter prospective cohort of patients with cirrhosis undergoing surveillance with MRI and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up.</p><p><strong>RESULTS: </strong>We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, and PNPLA3 and TMS6SF2 single nucleotide polymorphisms, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top six variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC 0.88, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases.</p><p><strong>CONCLUSIONS: </strong>This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development.</p>
2024-05-09 | MTBLS8764 | MetaboLights
Project description:Comprehensive LC-PRM-MS approach where a targeted parallel reaction monitoring (PRM) strategy was coupled to a powerful LC system to study the microheterogeneity of serum haptoglobin (Hp) extracted from 15 patients with cirrhosis and 15 with hepatocellular carcinoma (HCC).
2021-09-10 | PXD023719 | Pride
Project description:Despite advances in DNA methylome analyses of cells and tissues, current techniques for genome-scale profiling of DNA methylation in circulating cell-free DNA (ccfDNA) remain limited. Here we describe a methylated CpG tandems amplification and sequencing (MCTA-Seq) method that can detect thousands of hypermethylated CpG islands simultaneously in ccfDNA. This highly sensitive technique can work with genomic DNA as little as 7.5 pg, which is equivalent to 2.5 copies of the haploid genome. We have analyzed a cohort of tissue and plasma samples (n=151) of hepatocellular carcinoma (HCC) patients and control subjects, identifying dozens of high-performance markers in blood for detecting small HCC (<=3 cm). Among these markers, 4 (RGS10, ST8SIA6, RUNX2 and VIM) are mostly specific for cancer detection, while the other 15, classified as a novel set, are already hypermethylated in the normal liver tissues. Two corresponding classifiers have been established, combination of which achieves a sensitivity of 94% with a specificity of 89% for the plasma samples from HCC patients (n=36) and control subjects including cirrhosis patients (n=17) and normal individuals (n=38). Notably, all 15 alpha-fetoprotein-negative HCC patients were successfully identified. Comparison between matched plasma and tissue samples indicates that both the cancer and noncancerous tissues contribute to elevation of the methylation markers in plasma. MCTA-Seq will facilitate the development of ccfDNA methylation biomarkers and contribute to the improvement of cancer detection in a clinical setting.
2015-11-16 | GSE63775 | GEO